초록

This paper provides a framework for predictingstock magnitude and trend for making trading decisions bymaking use of a combination of Data Mining and Text Miningmethods. The prediction model predicts the stock market closingprice for a given trading day ‘D’, by analysing the informationrich unstructured news articles along with the historical stockquotes. In particular, we investigate the immediate impact of thenews articles on the time series based on Efficient MarketHypothesis (EMH).Key phrases provide semantic metadata thatsummarize and characterize documents. This frameworkincorporates Kea [1], an algorithm for automatically extractingkey phrases from news articles. The prediction power of theNeural Network is used for predicting the closing price for agiven trading day. The Neural Network is trained on theextracted key phrases and the stock quotes using the Backpropagation Algorithm.